Datasets¶

This chapter will briefly review datasets for music demixing by summarizing the previous tutorial. You can find a more detailed introduction and explanation from the previous tutorial.

Data for Music Demixing¶

At a high level, the inputs and outputs of a source separation model look like this:

Fig. 1 Inputs and outputs of a source separation model.¶

MUSDB18: tutorial¶

The MUSDB18 dataset [RLStoter+17] is one of the most widely used datasets for music demixing. For example, its uncompressed version (also known as MUSDB18-HQ [RLS+19]) was the official training dataset for Leaderboard A of the MDX challenge.

This section shows how to play with the musdb package.

Frist, install musdb pacakge.

pip install musdb

After the installation, please load musdb with download=True. This will download 7 seconds sample tracks of MUSDB18.

import musdb
mus = musdb.DB(download=True)

We can use mus as a iterator.

print(len(mus))

Let us load the first track of the MUSDB18 dataset

track = mus[0]
print(track)

A Classic Education - NightOwl

Let us listen to the mixture (i.e., the input audio in Fig 1!)

from IPython.display import Audio, display

display(Audio(track.audio.T, rate=track.rate))

Let us listen to the output audio tracks (i.e., the targets)

for source in track.sources.keys():
    print('source name: {}'.format(source))
    display(Audio(track.sources[source].audio.T, rate=track.rate))    

source name: vocals

source name: drums

source name: bass

source name: other

Thus, the input and output of the MUSDB18’s music demixing task are:

input: track.audio
output: {source: track.sources[source].audio for source in ['vocals', 'drums', 'bass', 'other']}

Quick overview of existing datasets¶

In the MDX challenge, participants must train their system on the training set of MUSDB18-HQ dataset (or MUSDB18 dataset) for Leaderboard A. For Leaderboard B, there have been no constraints in the choice of training data. (i.e., any available datasets can be used by the participants).

Here’s a quick overview of existing datasets released after 2015 for Music Demixing:

Dataset	Year	Instrument categories	Tracks	Avgerage duration (s)	Full songs	Stereo
DSD100	2015	4	100	251 \(\pm\) 60	✅	✅
MUSDB18	2017	4	150	236 \(\pm\) 95	✅	✅
MUSDB18-HQ	2019	4	150	236 \(\pm\) 95	✅	✅
Slakh2100	2019	34	2100	249	✅	❌

You can check the full list of datasets here. This extended table is based on: SigSep/datasets, and reproduced with permission.

A Primer on Source Separation

Datasets¶

Data for Music Demixing¶

MUSDB18: tutorial¶

Quick overview of existing datasets¶